Freeway merging bottlenecks (e.g., lane-drop, entry ramps) are major sources of congestion, where traffic breakdown is often triggered by complex interactions among merging vehicles. While congestion has been widely studied, existing models for bottleneck congestion frequently rely on simplified behavioral assumptions and do not adequately capture the mechanisms that lead to congestion onset. This limits the reliability of simulation tools used for operational analysis and policy evaluation. This project proposes a data-driven, simulation-based framework to model merging behavior and quantify its role in congestion formation at merging bottlenecks. Using high-resolution drone-based trajectory data collected during congestion onset, the study will characterize key aspects of human merging behavior, including gap acceptance, lane-changing dynamics, and speed adaptation. Additional drone data collection will be conducted as needed to enrich the dataset. A probabilistic model will be developed to estimate the likelihood of congestion onset as a function of traffic conditions and merging behavior. This model will be integrated into a microscopic traffic simulation platform to reproduce realistic merging dynamics and congestion formation as an emergent phenomenon. The resulting framework will provide transportation agencies with a practical tool for analyzing congestion mechanisms and evaluating operational strategies. The project also establishes a foundation for future research on control strategies in mixed traffic systems, including automated vehicle applications.